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Reinforcement Learning (RL)

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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/dongguanting/tool-star

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Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning

https://github.com/dongzhou-1996/sncoat

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Space Non-cooperative Object Active Tracking with Deep Reinforcement Learning

https://github.com/dorlivne/PoPS

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PoPS: Policy Pruning and Shrinking for Deep Reinforcement Learning

https://github.com/dornik/reagent

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ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning

https://github.com/dorothyduuu/swe-dev

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SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development

https://github.com/dos-group/magpie

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Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning

https://github.com/dpanigra/irs-partition

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An Intrusion Response System utilizing Deep Q-Networks and System Partitions

https://github.com/dppalomar/sparseindextracking

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Reinforcement Learning for Financial Index Tracking

https://github.com/dracosource/biddinggame

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Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games

https://github.com/dracosource/draco

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Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions

https://github.com/dragon-zhuang/bppo

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Behavior Proximal Policy Optimization

https://github.com/dragon-zhuang/reinformer

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Reinformer: Max-Return Sequence Modeling for Offline RL

https://github.com/drdh/synergy-rl

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Low-Rank Modular Reinforcement Learning via Muscle Synergy

https://github.com/drl-casia/autonomous-driving

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Lane Change Decision-Making through Deep Reinforcement Learning

https://github.com/drlinter/drlinter

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Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach

https://github.com/drmeerkat/Deep-Graph-Value-Network

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Towards Sample Efficient Agents through Algorithmic Alignment

https://github.com/dsbrown1331/CoRL2019-DREX

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Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations

https://github.com/dsbrown1331/aaai-2018-code

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Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning

https://github.com/dsbrown1331/broil

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Bayesian Robust Optimization for Imitation Learning

https://github.com/dsbrown1331/machine-teaching-irl

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Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications

https://github.com/dsshim0125/gaussian-ram

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Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement Learning

https://github.com/dsshim0125/s2p

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S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning

https://github.com/dt6a/actoreg

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The Role of Deep Learning Regularizations on Actors in Offline RL

https://github.com/dt6a/clorl

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Is Value Functions Estimation with Classification Plug-and-play for Offline Reinforcement Learning?

https://github.com/dt6a/rebrac

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Revisiting the Minimalist Approach to Offline Reinforcement Learning

https://github.com/dtak/POPCORN-POMDP

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POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning

https://github.com/dtak/mbrl-smdp-ode

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Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs

https://github.com/dtak/osiris

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State Relevance for Off-Policy Evaluation

https://github.com/dtch1997/rl_cbf

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Value Functions are Control Barrier Functions: Verification of Safe Policies using Control Theory

https://github.com/dti-research/SenseActExperiments

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A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots

https://github.com/duongnhatthang/llamagym

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM

https://github.com/duvenaud/relax

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Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

https://github.com/duyunshu/lucid-dreaming-for-exp-replay

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Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy

https://github.com/dvampire/earnmore

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Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools

https://github.com/dvij542/offtersim

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WROOM: An Autonomous Driving Approach for Off-Road Navigation

https://github.com/dvlab-research/arpo

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ARPO:End-to-End Policy Optimization for GUI Agents with Experience Replay

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